融合不确定性估计的端到端视频事件检测算法

庞枫骞; 赵鸿飞; 康营营

doi:10.13700/j.bh.1001-5965.2022.0897

融合不确定性估计的端到端视频事件检测算法

doi: 10.13700/j.bh.1001-5965.2022.0897

北方工业大学信息学院，北京 100144

基金项目: 国家自然科学基金(62001009)；北京市教育委员会科技计划一般项目(KM202210009003)；北方工业大学科研启动基金

详细信息

通讯作者:
E-mail： fqpang@ncut.edu.cn

中图分类号: TP751.1；TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-03
- 录用日期: 2022-12-09
- 网络出版日期: 2023-02-15
- 整期出版日期: 2024-12-31

Uncertainty estimation fused end-to-end video event detection algorithm

School of Information Science and Technology，North China University of Technology，Beijing 100144，China

Funds: National Natural Science Foundation of China (62001009); Research and Development Program of Beijing Municipal Education Commission (KM202210009003); Scientific Research Initiation Foundation of North China University of Technology

More Information

Corresponding author: E-mail:fqpang@ncut.edu.cn

摘要

摘要:
近年来，视频事件检测在计算机视觉领域受到了越来越多的关注。不确定性估计可以在输出检测结果不可靠时提醒决策系统或人员，从而减少决策失误。将不确定性估计引入事件检测任务，提出一种融合不确定性估计的端到端视频事件检测算法。该算法对预测视频事件的定位不确定性和分类不确定性进行估计，通过降低网络预测的不确定性优化网络性能。将不确定性估计和非极大值抑制策略相结合，进一步筛选出高质量预测框。实验结果表明：添加不确定性分支可提升模型的性能。在J-HMDB-21数据集上，所提算法在mAP50检测指标上相比先进算法提升了0.8%。在时空局部原子视觉(AVA)数据集上与其他的端到端算法相比，mAP50指标也得到了1.3%的提升。
- 视频事件检测 /
- 不确定性估计 /
- 端到端学习 /
- 视频目标检测 /
- 深度神经网络
Abstract:
In recent years, video event detection has attracted increasing attention in the field of computer vision. Uncertainty estimation can alert decision-making systems or personnel when the output detection results are unreliable, reducing decision-making errors. In this paper, we proposed an end-to-end video event detection algorithm that fuses uncertainty estimation into the event detection task. The proposed algorithm estimates the localization and classification uncertainty for the input video events and optimizes network performance by reducing the predicted uncertainty. In addition, this paper combines uncertainty estimation with the Non-Maximum Suppression strategy to further filter the high-quality prediction boxes. The experimental results show that adding an uncertainty branch can improve the algorithm’s performance. On the J-HMDB-21 dataset, the proposed algorithm improves the mAP50 detection index by 0.8% compared with advanced algorithms. On the atomic visual actions (AVA) dataset, compared with other end-to-end networks, the mAP50 index is also improved by 1.3%.
- video event detection /
- uncertainty estimation /
- end-to-end learning /
- video object detection /
- deep neural network

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图 1 UC-YOWO算法结构

Figure 1. UC-YOWO algorithm structure

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图 2 UC-YOWO 算法子模块结构

Figure 2. UC-YOWO algorithm sub-module structure

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图 3 不确定性损失函数设计

Figure 3. Uncertainty loss function design

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图 4 NMS 算法示意图

Figure 4. Schematic diagram of NMS algorithm

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图 5 不同λ参数的相对 mAP

Figure 5. Relative mAP of different parameters λ

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图 6 J-HMDB-21数据集^[13]检测结果

Figure 6. Test result in J-HMDB-21 data set^[13]

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图 7 不确定性值和 IoU 散点图

Figure 7. Scatter plot of uncertainty and IoU

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图 8 去除关键帧时的不确定性

Figure 8. Uncertainties while removing keyframes

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图 9 替换关键帧时的不确定性

Figure 9. Uncertainties while replacing keyframes

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图 10 UC-YOWO 算法对新类别的检测结果

Figure 10. UC-YOWO algorithm test results for new categories

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表 1 不同初始化方差对算法性能的影响

Table 1. Influence of different initialization variance on algorithm performance

σ₀	mAP50/%
σ₀	VIRAT^[23]	J-HMDB-21^[13]
	37.2	65.4
10⁻¹	37.1	65.1
10⁻²	37.4	65.5
10⁻³	37.9	66.9
10⁻⁴	36.9	66.7

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表 2 不同视频帧参数对算法性能的影响

Table 2. Influence of different video frame parameters on algorithm performance

帧长度/帧	采样间隔/帧	mAP50/%
帧长度/帧	采样间隔/帧	J-HMDB-21^[13]	AVA^[10]	VIRAT^[23]
8	$d = 1$	66.9	16.5	37.9
8	$d = 2$	65.8	16.0	35.0
8	$d = 3$	62.7	16.0	34.6
16	$d = 1$	74.8	17.8	41.0
32	$d = 1$	71.2	18.5	44.9

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表 3 现有不同算法在J-HMDB-21数据集^[13]的对比

Table 3. Comparison of different deep algorithms in J-HMDB-21 data set^[13]

算法	算法类型	数据类型	mAP50/%
Peng w/o MR^[9]	双阶段	V	56.9
Peng w/ MR^[9]	双阶段	V	58.5
T-CNN^[26]	双阶段	V	61.3
ACT^[27]	双阶段	V+F	65.7
P3D-CTN^[28]	单阶段	V	71.1
YOWO^[12]	单阶段	V	74.4
UC-YOWO	单阶段	V	74.7
UC-YOWO+Std-NMS	单阶段	V	75.2

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表 4 现有不同算法在AVA数据集^[10]的对比

Table 4. Comparison of different deep algorithms in AVA data set^[10]

算法	算法类型	数据类型	mAP50/%
I3D^[10]	双阶段	V+F	15.6
ACRN, S3D^[30]	双阶段	V+F	17.4
STEP, I3D^[31]	双阶段	V+F	18.6
RTPR^[29]	双阶段	V+F	22.3
LFB, R101+NL^[32]	双阶段(离线)	V	27.4
ACAR, R50, 8x8, (64-f)^[30]	双阶段(离线)	V	28.3
SlowFast, R50,8x8,(64-f)^[33]	双阶段(离线)	V	24.8
YOWO(32-f)^[12]	单阶段	V	18.3
UC-YOWO(32-f)	单阶段	V	18.5
UC-YOWO+StdNMS(32-f)	单阶段	V	19.6

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